




'''simple docstring'''



from argparse import ArgumentParser, Namespace

from ..utils import logging
from . import BaseTransformersCLICommand







def       _UpperCamelCase			(_lowerCamelCase				:  Namespace						)-> List[str]:




     '''simple docstring'''





     return ConvertCommand(
         args.model_type ,						args.tf_checkpoint ,						args.pytorch_dump_output ,						args.config ,						args.finetuning_task_name						)


UpperCAmelCase_							:	str	     =		'''
transformers can only be used from the commandline to convert TensorFlow models in PyTorch, In that case, it requires
TensorFlow to be installed. Please see https://www.tensorflow.org/install/ for installation instructions.
'''







class      lowerCAmelCase (			__lowerCAmelCase):


       @staticmethod
       def    lowerCAmelCase   ( __SCREAMING_SNAKE_CASE	)   ->	Tuple:



            '''simple docstring'''




            __snake_case            =	parser.add_parser(
                '''convert'''     ,	help='''CLI tool to run convert model from original author checkpoints to Transformers PyTorch checkpoints.'''     ,	)
            train_parser.add_argument('''--model_type'''     ,	type=__SCREAMING_SNAKE_CASE     ,	required=__SCREAMING_SNAKE_CASE     ,	help='''Model\'s type.'''	)
            train_parser.add_argument(
                '''--tf_checkpoint'''     ,	type=__SCREAMING_SNAKE_CASE     ,	required=__SCREAMING_SNAKE_CASE     ,	help='''TensorFlow checkpoint path or folder.'''	)
            train_parser.add_argument(
                '''--pytorch_dump_output'''     ,	type=__SCREAMING_SNAKE_CASE     ,	required=__SCREAMING_SNAKE_CASE     ,	help='''Path to the PyTorch saved model output.'''	)
            train_parser.add_argument('''--config'''     ,	type=__SCREAMING_SNAKE_CASE     ,	default=''''''     ,	help='''Configuration file path or folder.'''	)
            train_parser.add_argument(
                '''--finetuning_task_name'''     ,	type=__SCREAMING_SNAKE_CASE     ,	default=__SCREAMING_SNAKE_CASE     ,	help='''Optional fine-tuning task name if the TF model was a finetuned model.'''     ,	)
            train_parser.set_defaults(func=__SCREAMING_SNAKE_CASE	)


       def __init__( self     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	*__SCREAMING_SNAKE_CASE     ,	)   ->	List[str]:



            '''simple docstring'''




            __snake_case            =	logging.get_logger('''transformers-cli/converting'''	)

            self._logger.info(F'''Loading model {model_type}'''	)
            __snake_case            =	model_type
            __snake_case            =	tf_checkpoint
            __snake_case            =	pytorch_dump_output
            __snake_case            =	config
            __snake_case            =	finetuning_task_name




       def    lowerCAmelCase   ( self	)   ->	int:



            '''simple docstring'''




            if self._model_type == "albert":
                 try:
                      from ..models.albert.convert_albert_original_tf_checkpoint_to_pytorch import (
                          convert_tf_checkpoint_to_pytorch,
                      )
                 except ImportError:
                      raise ImportError(__SCREAMING_SNAKE_CASE	)

                 convert_tf_checkpoint_to_pytorch(self._tf_checkpoint     ,	self._config     ,	self._pytorch_dump_output	)
            elif self._model_type == "bert":
                 try:
                      from ..models.bert.convert_bert_original_tf_checkpoint_to_pytorch import (
                          convert_tf_checkpoint_to_pytorch,
                      )
                 except ImportError:
                      raise ImportError(__SCREAMING_SNAKE_CASE	)

                 convert_tf_checkpoint_to_pytorch(self._tf_checkpoint     ,	self._config     ,	self._pytorch_dump_output	)
            elif self._model_type == "funnel":
                 try:
                      from ..models.funnel.convert_funnel_original_tf_checkpoint_to_pytorch import (
                          convert_tf_checkpoint_to_pytorch,
                      )
                 except ImportError:
                      raise ImportError(__SCREAMING_SNAKE_CASE	)

                 convert_tf_checkpoint_to_pytorch(self._tf_checkpoint     ,	self._config     ,	self._pytorch_dump_output	)
            elif self._model_type == "t5":
                 try:
                      from ..models.ta.convert_ta_original_tf_checkpoint_to_pytorch import convert_tf_checkpoint_to_pytorch
                 except ImportError:
                      raise ImportError(__SCREAMING_SNAKE_CASE	)

                 convert_tf_checkpoint_to_pytorch(self._tf_checkpoint     ,	self._config     ,	self._pytorch_dump_output	)
            elif self._model_type == "gpt":
                 from ..models.openai.convert_openai_original_tf_checkpoint_to_pytorch import (
                     convert_openai_checkpoint_to_pytorch,
                 )

                 convert_openai_checkpoint_to_pytorch(self._tf_checkpoint     ,	self._config     ,	self._pytorch_dump_output	)
            elif self._model_type == "transfo_xl":
                 try:
                      from ..models.transfo_xl.convert_transfo_xl_original_tf_checkpoint_to_pytorch import (
                          convert_transfo_xl_checkpoint_to_pytorch,
                      )
                 except ImportError:
                      raise ImportError(__SCREAMING_SNAKE_CASE	)

                 if "ckpt" in self._tf_checkpoint.lower():
                      __snake_case            =	self._tf_checkpoint
                      __snake_case            =	''''''
                 else:
                      __snake_case            =	self._tf_checkpoint
                      __snake_case            =	''''''
                 convert_transfo_xl_checkpoint_to_pytorch(
                     __SCREAMING_SNAKE_CASE     ,	self._config     ,	self._pytorch_dump_output     ,	__SCREAMING_SNAKE_CASE	)
            elif self._model_type == "gpt2":
                 try:
                      from ..models.gpta.convert_gpta_original_tf_checkpoint_to_pytorch import (
                          convert_gpta_checkpoint_to_pytorch,
                      )
                 except ImportError:
                      raise ImportError(__SCREAMING_SNAKE_CASE	)

                 convert_gpta_checkpoint_to_pytorch(self._tf_checkpoint     ,	self._config     ,	self._pytorch_dump_output	)
            elif self._model_type == "xlnet":
                 try:
                      from ..models.xlnet.convert_xlnet_original_tf_checkpoint_to_pytorch import (
                          convert_xlnet_checkpoint_to_pytorch,
                      )
                 except ImportError:
                      raise ImportError(__SCREAMING_SNAKE_CASE	)

                 convert_xlnet_checkpoint_to_pytorch(
                     self._tf_checkpoint     ,	self._config     ,	self._pytorch_dump_output     ,	self._finetuning_task_name	)
            elif self._model_type == "xlm":
                 from ..models.xlm.convert_xlm_original_pytorch_checkpoint_to_pytorch import (
                     convert_xlm_checkpoint_to_pytorch,
                 )

                 convert_xlm_checkpoint_to_pytorch(self._tf_checkpoint     ,	self._pytorch_dump_output	)
            elif self._model_type == "lxmert":
                 from ..models.lxmert.convert_lxmert_original_tf_checkpoint_to_pytorch import (
                     convert_lxmert_checkpoint_to_pytorch,
                 )

                 convert_lxmert_checkpoint_to_pytorch(self._tf_checkpoint     ,	self._pytorch_dump_output	)
            elif self._model_type == "rembert":
                 from ..models.rembert.convert_rembert_tf_checkpoint_to_pytorch import (
                     convert_rembert_tf_checkpoint_to_pytorch,
                 )

                 convert_rembert_tf_checkpoint_to_pytorch(self._tf_checkpoint     ,	self._config     ,	self._pytorch_dump_output	)
            else:
                 raise ValueError(
                     '''--model_type should be selected in the list [bert, gpt, gpt2, t5, transfo_xl, xlnet, xlm, lxmert]'''	)







"""simple docstring"""


from __future__ import annotations

from numpy import array, cos, cross, floataa, radians, sin
from numpy.typing import NDArray





def      lowerCamelCase_					(   __lowerCAmelCase  ,       __lowerCAmelCase  ,       __lowerCAmelCase = False							)				->   list[float]:




						'''simple docstring'''



						if radian_mode:
												return [magnitude * cos(__lowerCAmelCase							), magnitude * sin(__lowerCAmelCase							)]
						return [magnitude * cos(radians(__lowerCAmelCase							)							), magnitude * sin(radians(__lowerCAmelCase							)							)]





def      lowerCamelCase_					(   __lowerCAmelCase  ,       __lowerCAmelCase  ,       __lowerCAmelCase = 10**-1							)				->   bool:




						'''simple docstring'''



						lowerCamelCase__   =cross(__lowerCAmelCase  ,       __lowerCAmelCase							)
						lowerCamelCase__   =sum(__lowerCAmelCase							)
						return abs(__lowerCAmelCase							) < eps


if __name__ == "__main__":
	# Test to check if it works
	a				=array(
	    [
	        polar_force(718.4, 180 - 30),
	        polar_force(879.54, 45),
	        polar_force(100, -90),
	    ]
	)

	a				=array([[0, 0], [0, 0], [0, 0]])

	assert in_static_equilibrium(forces, location)

	# Problem 1 in image_data/2D_problems.jpg
	a				=array(
	    [
	        polar_force(30 * 9.81, 15),
	        polar_force(215, 180 - 45),
	        polar_force(264, 90 - 30),
	    ]
	)

	a				=array([[0, 0], [0, 0], [0, 0]])

	assert in_static_equilibrium(forces, location)

	# Problem in image_data/2D_problems_1.jpg
	a				=array([[0, -2000], [0, -1200], [0, 1_5600], [0, -1_2400]])

	a				=array([[0, 0], [6, 0], [10, 0], [12, 0]])

	assert in_static_equilibrium(forces, location)

	import doctest

	doctest.testmod()


